model.py

import torch
import torch.nn as nn

class MyNetwork(nn.Module):
    def __init__(self):
        super().__init__()
        self.model = nn.Sequential(

            nn.Conv2d(3, 32, 5, padding=2),
            nn.BatchNorm2d(32),
            nn.ReLU(),
            nn.MaxPool2d(2),

            nn.Conv2d(32, 64, 5, padding=2),
            nn.BatchNorm2d(64),
            nn.ReLU(),
            nn.MaxPool2d(2),

            nn.Conv2d(64, 128, 5, padding=2),
            nn.BatchNorm2d(128),
            nn.ReLU(),
            nn.MaxPool2d(2),

            nn.Conv2d(128, 256, 5, padding=2),
            nn.BatchNorm2d(256),
            nn.ReLU(),
            nn.MaxPool2d(2),

            nn.Flatten(),
            nn.Linear(1024, 256),
            nn.ReLU(),
            nn.Dropout(0.5),
            nn.Linear(256, 10)
        )


    def forward(self, x):
        x = self.model(x)
        return x

if __name__ == '__main__':
    mynetwork = MyNetwork()
    input = torch.ones((64, 3, 32, 32))
    output = mynetwork(input)
    print(output.shape)
    total_params = sum(p.numel() for p in mynetwork.parameters())
    print(f"Total params：{total_params}")
    print(f"Total params：{total_params / 1000000}M")